Machine Learning in the Real World

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Machine learning is a type of artificial intelligence that allows computers to learn from data and make decisions or predictions without being explicitly programmed. It's used all around us—often without us even realizing it. For example, streaming services like Netflix or Spotify use machine learning to recommend shows or songs based on your viewing or listening habits. In healthcare, it helps doctors predict patient outcomes by analyzing patterns in medical records and test results. Even self-driving cars rely on machine learning to recognize road signs, avoid obstacles, and make real-time driving decisions. By finding patterns in large amounts of data, machine learning is helping industries become smarter, faster, and more personalized.

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Developing a true A.I. score prediction model isn’t as simple as flipping a switch—it starts with one critical requirement:  massive amounts of detailed data. Most golf tech companies, especially those focused on tee time booking, GPS yardages, or swing analysis, simply don’t collect enough hole-by-hole scoring history across a wide population of golfers. Without that kind of data—spanning player types and course differences—there’s nothing meaningful for machine learning to analyze. With its massive volume of league golf data, Handicomp does not have this problem.

Building a predictive model also requires significant technical expertise, infrastructure, and time to train, test, and refine the algorithm. That’s why so few have ventured into this space. The companies that do have the data—and know how to use it—are in a unique position to lead the future of A.I.-powered golf performance.

Another issue for some technical companies is that they have a relationship with the USGA that allows them to interface with GHIN, which then contractually precludes them from developing alternative handicapping systems to what the USGA offers.

Developed by Handicomp, the A.I. score prediction model uses advanced machine learning to estimate a golfer’s upcoming round—hole by hole—with exceptional accuracy. Drawing from decades of golf data expertise, the model analyzes a combination of player characteristics, recent performance trends, and course-specific data to make its predictions. At the start of each round, it processes these inputs and generates an expected score for each hole, delivering a level of precision, adaptability, and personalization that traditional systems simply can’t match. 

Data Collection & Structuring

• To train the model, a large-scale database was created using more than 100-million hole scores from over 250,000 golfers. Each golfer’s historical performance was recorded at the hole level across multiple rounds.
• Course data is also integrated, including but not limited to the following:  Hole yardages, par, hole handicaps, course difficulty, geographic location, and statistical hole difficulty (measured as average score and deviation from net par). Additional inputs include golfer demographics (gender, age, skill level), providing further context for performance modeling.

Feature Engineering & Data Preparation

• The raw data is cleaned, transformed, and structured into usable “features” that the machine learning model can interpret. Key steps include normalizing data for consistency across golfers and courses, creating derived variables like rolling averages and trend lines, and organizing time-sequenced data to reflect performance progression.
• Databricks serves as the machine learning platform used to process and prepare this data at scale.

Model Training & Learning Approach

• A supervised learning model is used. Here's how:  The system goes back in time and uses a golfer’s known historical performance to predict the next score—a score that already exists in the data. This allows the model to learn from real-world outcomes by comparing predictions to actual results.
• The model detects relationships between golfer-specific trends (e.g., rolling averages, performance variability), hole and course difficulty, score type, familiarity with course and tee, and recency of play (e.g., days since last round).  These and many other variables allow the model to fine-tune its predictions based on a golfer’s evolving game.

Score Prediction Formula Generation

• Once trained, the model generates a predictive formula that looks back at up to 50 rounds and dynamically estimates expected scores for upcoming rounds. The formula isn’t fixed—it continues to evolve as new rounds are entered and fresh data is ingested. This ensures the prediction stays relevant to the golfer’s current ability and playing patterns. 

Application & Continuous Learning

•  The model delivers hole-by-hole predictions, which are then summed to produce a total round prediction. This level of detail goes far beyond traditional handicapping systems, which rely on static averages.
• The formula is integrated into apps and platforms where golfers, leagues, and clubs can access their real-time predictions. As new scores are posted, the model updates—learning from recent play and refining accuracy on the fly.
• Round handicaps are calculated simply by subtracting course par from the total predicted score.

By replacing the traditional, static handicap formula with a live, adaptive prediction engine, this method offers a deeper, more personalized view of a golfer’s performance. It empowers golfers to set better goals, track real progress, and engage in fairer, more meaningful competition. 

Handicomp released the first version of its A.I. Score Prediction Model, V23, in the Fall of 2023, which demonstrated significantly improved predictive accuracy in internal testing compared to traditional handicap benchmarks. V24, which launched in Spring of 2024, introduced enhancements that significantly improved precision and accuracy—especially for high-handicap golfers. Now, with the recent release of V25 in Spring of 2025, both previous versions have been fully replaced. V25 delivers substantially better performance across the board and, in our view, represents a level of accuracy that will be difficult to surpass.